Support vector machine in the anticipation of currency markets

Nadh, V Lalithendra; Prasad, Gudapati Syam

doi:10.14419/ijet.v7i2.7.10262

Cited by 6 publications

(4 citation statements)

References 2 publications

(2 reference statements)

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“…Fig. 3 Based on the findings, the results are consistent with the results from [18] and [19], which demonstrates that SVM has better performance in stock forecast compared to XGB and LR. This implies that Malaysian stocks are more compatible with the application of SVM than the other two ML algorithms in a trading strategy.…”

Section: Resultssupporting

confidence: 80%

“…The paper shows that SVM has better efficiency in stock forecast among the selected ML algorithms in this study for the Chinese stock market, followed by XGB and LR. Another paper by [19] stated that it is useful to use SVM in time series forecasting as it has a small MSE value. SVM is also efficient in training the data; consequently, it has a short training time.…”

Section: B Related Workmentioning

confidence: 99%

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Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

Halul,

Kamil

2022

MJoSHT

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Nowadays, Machine Learning (ML) plays a significant role in the economy, especially in the stock trading strategy. However, there is an inadequate extensive data analysis using various ML methods. Previous findings usually focus on the forecasting stock index or selecting a limited number of stocks with restricted features. Therefore, the contribution of this paper focused on evaluating different supervised learning algorithms, namely Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), on a big dataset from 28 stocks in Bursa Malaysia. By setting their parameter along and using Walk-Forward Analysis (WFA) method, the trading signal was evaluated based on Accuracy Rate, Precision Rate, Recall Rate, and F1 Score. For stock trading strategies in Malaysia in particular, the findings of this study show that SVM has a better performance compared to LR and XGB in time series forecasting. The ML algorithms have values ranging from 53% to 66% for Accuracy Rate (AR), Recall Rate (RR), and F1 Score (F1). In addition, SVM has the highest Precision Rate (PR) of 73% among the ML algorithms.

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Section: Resultssupporting

confidence: 80%

Section: B Related Workmentioning

confidence: 99%

Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

Halul,

Kamil

2022

MJoSHT

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“…The main idea of these algorithms lies on the motto "let the data talk", as they avoid potential biases introduced by researchers such as the selection of variables, the level of significance to obtain statistical inference or the way in which we discretize continuous variables. Indeed, the discretization problem previously described is not exclusive of the IOp literature, as it is highlighted by the use of ML techniques in many different fields, such as biomedicine (Lutsgarten et al 2011), genetics (Gallo et al 2016), the stock market dynamics (Lalithendra and Prasad 2018) or the price of gold (Banerjee et al 2019).…”

Section: Machine Learning and Inequality Of Opportunitymentioning

confidence: 99%

Inheritances and wealth inequality: a machine learning approach

Salas-Rojo

Rodríguez

2022

J Econ Inequal

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This paper explores the relationship between received inheritances and the distribution of wealth (financial, non-financial and total) in four developed countries: the United States, Canada, Italy and Spain. We follow the inequality of opportunity (IOp) literature and − considering inheritances as the only circumstance− we show that traditional IOp approaches can lead to non-robust and arbitrary measures of IOp depending on discretionary cut-off choices of a continuous circumstance such as inheritances. To overcome this limitation, we apply Machine Learning methods (‘random forest’ algorithm) to optimize the choice of cut-offs and we find that IOp explains over 60% of wealth inequality in the US and Spain (using the Gini coefficient), and more than 40% in Italy and Canada. Including parental education as an additional circumstance −available for the US and Italy− we find that inheritances are still the main contributor. Finally, using the S-Gini index with different parameters to weight different parts of the distribution, we find that the effect of inheritances is more prominent at the middle of the wealth distribution, while parental education is more important for the asset-poor.

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“…The past extreme events like BOB (06), Thane and Vardahare validated and the results are verified by the parameters like homogeneity and completeness. This paper addresses the use of ML algorithms to filter and visualize the extreme weather event [8]. In the proposed system, AFM technique is used to filter the events and based on the results the class labels are assigned.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Climate Change using SVM and Naïve Bayes Machine Learning Algorithms

Karthikeyan¹

2021

TURCOMAT

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Various reasons are there in failures of Intergovernmental Panel on Climate Change (IPCC) simulation model for prediction of climate change. For the better understanding of IPCC model’s failures by researchers, an improvement is qualitative and quantitative analysis is required and to be implemented. We come across a continuous crashes in simulation of Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4), while measuring the impact of ocean model parameter uncertainties on weather simulations, during the period of uncertainty quantification (UQ) ensemble. This manuscript analyse the different machine learning algorithms, such as, Random forest, Linear Regression, k-means and naïve-bayes algorithms. From machine learning, a quality classifier called support vector machine (SVM) classification is used to predict and quantify the failures probability as a function of the values of POP2 parameters. Apart from quantification and prediction, this method performs a better understanding in simulation crashes in other complex geo-scientific models.

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Support vector machine in the anticipation of currency markets

Cited by 6 publications

References 2 publications

Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

Inheritances and wealth inequality: a machine learning approach

Prediction of Climate Change using SVM and Naïve Bayes Machine Learning Algorithms

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